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   <div id="projectname">Parallel Gaussian Process Regression
   &#160;<span id="projectnumber">1.0.0</span>
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   <div id="projectbrief">The implementation of parallel Gaussian process (GP) regression is based on the following publication: Jie Chen, Nannan Cao, Kian Hsiang Low, Ruofei Ouyang, Colin Keng-Yan Tan &amp; Patrick Jaillet. Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013), Bellevue, WA, Jul 11-15, 2013.</div>
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&#160;<ul>
<li>elsolve()
: <a class="el" href="classpgpr__chol.html#a365839254c16b047b2e36a7937b8593f">pgpr_chol</a>
</li>
<li>end()
: <a class="el" href="classpgpr__timer.html#ac81e09244717a72ce57242e86aa6c200">pgpr_timer</a>
</li>
<li>getAttr()
: <a class="el" href="classpgpr__domain.html#a55b740e3d3a4a0c5bf86cd14895cd76b">pgpr_domain</a>
</li>
<li>getDist()
: <a class="el" href="classpgpr__domain.html#ac24189f56e27aad49dee703f5a58cc9b">pgpr_domain</a>
</li>
<li>getNeighbour()
: <a class="el" href="classpgpr__domain.html#ae07953124466fffafd45013bd7917ddd">pgpr_domain</a>
</li>
<li>getRandomBlk()
: <a class="el" href="classpgpr__data.html#a569dc41fa124c739d28d05d56874d4c4">pgpr_data</a>
</li>
<li>getRandomWalk()
: <a class="el" href="classpgpr__data.html#a1b0b0b000a221c160339ea4207910753">pgpr_data</a>
</li>
<li>getSt()
: <a class="el" href="structt__state.html#ac9637b03144741bb23da24c8ec61359b">t_state</a>
</li>
<li>getTestSet()
: <a class="el" href="classpgpr__data.html#accb6162698eefa8c8826205a21ad18ab">pgpr_data</a>
</li>
<li>getTrainSet()
: <a class="el" href="classpgpr__data.html#aa826857a19458c7ec5dde1b9109f3b07">pgpr_data</a>
</li>
<li>init()
: <a class="el" href="structt__state.html#ac2e30425ac01de134c164fb1f2d11603">t_state</a>
</li>
<li>inverse()
: <a class="el" href="classpgpr__chol.html#a8da419135a59362c31674091b0bfcf89">pgpr_chol</a>
</li>
<li>logdet()
: <a class="el" href="classpgpr__chol.html#a72827b33f276569fa6c3c8f2af80c40e">pgpr_chol</a>
</li>
<li>pgpr_chol()
: <a class="el" href="classpgpr__chol.html#ade89a39def082f6de79df242a39fad67">pgpr_chol</a>
</li>
<li>pgpr_cov()
: <a class="el" href="classpgpr__cov.html#addb951bbf66b46728f94c6fab2d9b39a">pgpr_cov</a>
</li>
<li>pgpr_data()
: <a class="el" href="classpgpr__data.html#aa6879cea28bfba9c8a3fbd87df02a92c">pgpr_data</a>
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<li>pgpr_domain()
: <a class="el" href="classpgpr__domain.html#ac527084df0c657962f29a554da0b3eea">pgpr_domain</a>
</li>
<li>pgpr_parse()
: <a class="el" href="classpgpr__parse.html#a3e6e01f2423c4a3a48dcd134d0a86385">pgpr_parse</a>
</li>
<li>pgpr_plma()
: <a class="el" href="classpgpr__plma.html#a91e6492d35e220988b68d49cfe851d61">pgpr_plma</a>
</li>
<li>pgpr_timer()
: <a class="el" href="classpgpr__timer.html#a71561182800d5c213e9c5003c9b2e893">pgpr_timer</a>
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<li>plma_regr()
: <a class="el" href="classpgpr__plma.html#a52c35beeae947892f4f22145a1d91953">pgpr_plma</a>
</li>
<li>selMaxVar()
: <a class="el" href="classpgpr__data.html#accef69857141cfa9e7b415edab6e2899">pgpr_data</a>
</li>
<li>selRand()
: <a class="el" href="classpgpr__domain.html#a814453a62ebc1ae9b4e420323bf013c2">pgpr_domain</a>
</li>
<li>solve()
: <a class="el" href="classpgpr__chol.html#a567b23e2e93a85675de26550d5e4cb38">pgpr_chol</a>
</li>
<li>start()
: <a class="el" href="classpgpr__timer.html#ae30d8bfbf046764791f934d198f122b3">pgpr_timer</a>
</li>
<li>t_state()
: <a class="el" href="structt__state.html#a42c55e66207116e6e78283fd50414a1c">t_state</a>
</li>
<li>upSt()
: <a class="el" href="structt__state.html#ad627540f74cd0836ea8b32ec9dd1c299">t_state</a>
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